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Improving the quality of European monthly unemployment statistics

Improving the quality of European monthly unemployment statistics. Nicola Massarelli, Eurostat Q2014 - European Conference on Quality in Official Statistics Vienna , 3-5 June 2014. UNEMPLOYMENT RATES. KEY WORDS. TIME SERIES. QUALITY FRAMEWORK. Main features.

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Improving the quality of European monthly unemployment statistics

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  1. Improving the quality of European monthly unemployment statistics Nicola Massarelli, Eurostat Q2014 - European Conference on Quality in Official Statistics Vienna, 3-5 June 2014

  2. UNEMPLOYMENT RATES KEY WORDS TIME SERIES QUALITY FRAMEWORK

  3. Main features • ILO unemploymentrates and levels • Total, plus age and gender breakdown • NSA, SA, TREND • Monthly, quarterly, yearly • T+30days • EU28, EA18, MS • Levels, M-M and Y-Y changes

  4. Current production • Ownership: about 50-50 Eurostat-MS • 3 mainmethods for unadjustedseries • Pure monthly LFS • 3 monthrollingquarters of LFS data • Temporaldisaggregation(Quarterly LFS + monthlyadministrative data) • Publication of adjustedseries: SA, but trends for 4 countries

  5. How temporal disaggregation works Bulgaria, number of male unemployed aged 25-74, NSA (thousands)

  6. Quality concerns • Volatility • Revisions • Turning points identification • Timeliness

  7. Developing a quality framework • Goal: • Provideacceptancecriteria • Compare series • Structure: • Define appropriate indicators for each quality dimension • Synthetic indicator vs. scoreboard • Acceptancethresholds

  8. Volatility: big foot effect

  9. Volatility: pitching & roller coaster effects

  10. Measuring volatility • Big foot effect: STDev of M-M and Q-Q changes • Thresholds: 0.25 / 0.63 • Pitchingeffect:% signinversions • Threshold: 20% • Roller coastereffect: % double large inversions • Large: ≥0.2 p.p. for M, ≥ 0.3 p.p. for Q • Threshold: 0%

  11. Measuring revisions • Focus on last data point (headline) • Averageabsoluterevision of the level • Maxabsoluterevision of the level • STDevrevisionM-M change • % signinconsistency of M-M changes Whichthresholds?

  12. Turning point identification

  13. Unemployment rate: delay in the identification of turning points (monthly vintages)

  14. Summary: no perfect approach

  15. How to discriminate? • Do we focus on the right qualityconcerns? • Syntheticindicator or scoreboard? • Whichindicators? • Whichthresholds for acceptance? • Whichweights for indicatorsand qualitydimensions?

  16. Possible synthetic indicator: RMSE volatility + revisions

  17. THANK YOU FOR YOUR ATTENTION AND YOUR FEEDBACK nicola.massarelli@ec.europa.eu

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